Testing the market efficiency by mean absolute deviation

2017 ◽  
Vol 24 (7) ◽  
pp. 2049-2062 ◽  
Author(s):  
Louie Ren ◽  
Peter Ren
Keyword(s):  
Time Series ◽  
Market Efficiency ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Content Type ◽  
Accuracy Measure ◽  
The Mean ◽  
Practical Implications

Purpose Numerous articles have been written to prove or to disapprove the hypothesis of market efficiency. The purpose of this paper is to apply the forecast accuracy measure, mean absolute deviation (MAD), to check the validity of the hypothesis. Design/methodology/approach Forecast accuracies from applying different simple moving average methods to independently identically distributed (i.i.d.) or near i.i.d. normal time series are assessed by MAD. When moving period n is greater than m, then the mean of the MADs from the MA with n moving periods will be smaller than the mean of the MADs from the MA with m moving periods. Findings In this study, when different MAs are applied to four near i.i.d. finance time series from Fama’s papers, the MAD cannot distinguish the differences among MA methods with various moving periods. This contradiction implies that the four finance time series in Fama’s papers may not be i.i.d and implies that the market is not efficient. Research limitations/implications The finding is only based on simulation and four near i.i.d. time series studied in Fama’s papers in 1965 and 1970. Practical implications The study shows that that the differences of the rates of returns from Johns Manville, Goodyear, Owens Illinois, and General Electric studied are not i.i.d. and that the market is not efficient. It refutes what Fama (1965, 1970) has claimed. Social implications When the market is not efficient, investors may gain profit from the market. Originality/value Based on the literature review, this is the first study to use the forecast accuracy measure, MAD, for market efficiency.

scholarly journals Predicting Movement of Stock of Apple Inc. using Sutte Indicator

2017 ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Percentage Error ◽  
Decision Making Process ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Investment Decision Making ◽  
The Mean

The purpose of this study is to apply technical analysis e.g. Sutte Indicator in Stock Market that will assist in the investment decision-making process to buy or sell of stocks. This study took data from Apple Inc. which listed in the NasdaqGS in the period of 1 January 2008 to 26 September 2016. Performance of the Sutte Indicator can be seen with comparison with other technical analysis e.g. Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). Comparison of the reliability of prediction from Sutte Indicator, SMA, and MACD using the mean of square error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE).

scholarly journals SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN MODEL DENGAN MENGGUNAKAN METODE TIME SERIES

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Noer Chamid ◽  
Muhammad Ainul Yaqin ◽  
Nailul Izzah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Trend Line ◽  
Absolute Percentage Error

Analisis time series antara lain memahami dan menjelaskan mekanisme tertentu, meramalkan suatu nilai di masa depan dan mengoptimalkan sistem kendali. Dalam pengambilan keputusan yang menggunakan analisis time series tersebut perlu menggunakan software yang prabayar seperti Minitab, SPSS dan SAS sehingga perlu pembuatan sistem informasi yang mendukung keputusan dalam analisis tersebut. Sistem informasi yang dibuat tersebut akan dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah atau data lainnya. Model yang digunakan dalam menduga adalah dengan menggunakan 4 (empat) metode, yaitu : Metode Moving Average, Metode Eksponential Smooting, Metode Linier Trend Line dan Seasonal Adjusment. Dari 4 (empat) metode tersebut, dapat dipilih model yang terbaik dengan menggunakan kriteria menentukan nilai Mean Absolute Deviation (MAD) dan Mean Absolute Percentage Error (MAPE) yang terkecil. Sistem informasi yang dibuat tersebut sudah dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah. Sistem Pendukung Keputusan ini dapat dijadikan sebagai tool dalam membuat rekomendasi sebuah keputusan.Kata Kunci: Time Series, Sistem Pendukung Keputusan, Pendapatan Asli Daerah                                                                       

An empirical analysis of funds’ alternative measures in the mean absolute deviation (MAD) framework

2015 ◽  
Vol 10 (4) ◽  
pp. 726-746 ◽  
Author(s):  
Mohammad Reza Tavakoli Baghdadabad
Keyword(s):  
Linear Programming ◽  
Mutual Funds ◽  
Close Correlation ◽  
Programming Models ◽  
Mean Absolute Deviation ◽  
Information Ratio ◽  
Absolute Deviation ◽  
Content Type ◽  
Portfolio Managers ◽  
The Mean

Purpose – The purpose of this paper is to provide an attempt to evaluate the risk-adjusted performance of international mutual funds using the risk statistic generated by the mean absolute deviation (MAD) and promote the ability of portfolio managers and investors to make the logical decisions for selecting different funds using the new optimized measures. Design/methodology/approach – This study evaluates the performance of 50 international mutual funds using optimized risk-adjusted measures by the MAD over the monthly period 2001-2010. Using 50 linear programming models, the MAD is first computed by the linear programming models, and then seven performance measures of Treynor, Sharpe, Jensen’s α, M2, information ratio (IR), MSR, and FPI are optimized and proposed by the MAD to evaluate the mutual funds. Findings – The empirical evidence detects that the MAD is an important determinant to evaluate the funds’ performance. Using the MAD statistic, this paper shows that new optimized measures are mostly over-performed by the benchmark index; in addition, these optimized measures have close correlation with each other. The results, therefore, detect the importance of using new optimized measures in evaluating the mutual funds’ performance. Practical implications – The result of this study can be directly used as an initial data for decision of investors and portfolio managers who are seeking the possibility of participating in the global stock market by the international mutual funds. Originality/value – This paper is the first study which optimizes the variance of returns in the MAD framework for each fund to propose new seven optimized measures of Treynor, Sharpe, Jensen’s α, M2, IR, MSR, and FPI.

Optimal Production Inventory Policies for Operations: A Case Study of PVC Pipes Production

2013 ◽  
Vol 824 ◽  
pp. 536-543
Author(s):  
Harold C. Godwin ◽  
Uchendu O. Onwurah
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Manufacturing Sector ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Independent Variables ◽  
Production Inventory ◽  
F Distribution

This study focuses on solving the problem of overstocking and under stocking of production inventory in manufacturing sector. To ensure effective management of inventory in manufacturing sector, three years production data were gathered and properly analyzed using multiple linear regression analysis and time series forecasting methods. A multiple linear regression model was developed in MINITAB software to make prediction for inventory requirements. From the result, the coefficient of determination (R2) is 1.00, the adjusted R2 is 1.00, F-distribution is 4.212 x 107 which is greater than any value in F-distribution table, and all these show a very strong relationship between the dependent variable and the independent variables. Also, a Time series analysis was done to make forecast of monthly inventory requirements for both raw materials and finished products. Trend analysis and Moving Average method were used in Time series forecasting, and lower Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) were used as criteria for selecting the method that gives the best forecast. From the results obtained, Trend analysis gave MAPE 13% and MAD 2350, while Moving Average gave MAPE 14% and MAD 2574. This work adds to growing body of literatures on data driven inventory management by utilizing historical data in customized software for generation of models that can effectively make forecast of inventory requirements in manufacturing sector. Nomenclature: a = Value of yt at t = 0; b = Trend Value; MA= Moving Average; MAD = Mean Absolute Deviation MAPE =Mean Absolute Percentage Error; N = Number of periods; t = Period Yt = Forecast for period t y = Monthly Quantity of Product Produced α=regression constant β1-βk=Coefficients of the independent variables

scholarly journals COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

2018 ◽  
Vol 47 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Syed Misbah Uddin ◽  
Aminur Rahman ◽  
Emtiaz Uddin Ansari
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

scholarly journals PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

2020 ◽  
Vol 6 (3) ◽  
pp. 29-36
Author(s):  
Deddy Kusbianto ◽  
Agung Pramudhita ◽  
Nurhalimah
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

scholarly journals Peramalan Permintaan Menggunakan Time Series Forecasting Model Untuk Merancang Resources Yang Dibutuhkan IKM Percetakan

2021 ◽  
Vol 2 (2) ◽  
pp. 105-115
Author(s):  
Anna Nita Kusumawati ◽  
Muhammad Ghofur ◽  
Mega Anggraeni Putri ◽  
Zaki Abdullah Alfatah ◽  
Mu’adzah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Forecasting Model ◽  
Mean Absolute Deviation ◽  
Microsoft Excel ◽  
Absolute Deviation ◽  
Tracking Signal

CV Adi Jaya merupakan perusahaan manufaktur yang bergerak dalam industri percetakan. Dalam memastikan kapasitas produksi yang ada dapat memenuhi permintaan konsumen diperlukan metode peramalan yang akurat. Tujuan penelitian ini adalah untuk menentukan metode peramalan terbaik dan meramalkan permintaan konsumen pada tahun 2021. Penelitian ini adalah penelitian deskriptif dengan metode yang digunakan untuk menyelesaikan permasalahan tersebut adalah metode time series dan teknik analisis menggunakan Microsoft Excel. Tingkat error yang dihasilkan dari perhitungan metode peramalan diketahui dengan penghitungan kesalahan mean absolute deviation (MAD), kemudian didapatkan tracking signal. Berdasarkan hasil analisis data, diketahui metode peramalan time series terbaik untuk meramalkan penjualan produk kemasan berbahan plastik adalah metode centered moving average 3 periode. Metode ini dipilih karena memiliki tingkat error paling rendah jika dibandingkan dengan metode lain yang dianalisis, yaitu dengan nilai MAD 65.773,08333 dan nilai tracking signal yang berada dalam batas pengendalian. Sehingga metode CMA 3 periode dapat digunakan dalam peramalan. Dari metode CMA 3 periode didapatkan peramalan permintaan konsumen di bulan Januari sampai Mei 2021 sebanyak 883.780 pcs setiap bulannya. Sehingga diperkirakan perlu adanya overtime yang tidak terlalu banyak untuk memenuhi peramalan permintaan pada bulan Januari-Mei 2021 karena kapasitas perusahaan perbulan hanya 875.000 pcs.

scholarly journals Smoothing Before Estimating Uncertainty, Scaling and Intermittency: Application to Short Heart Rate Signals

Fractals ◽  
2003 ◽  
Vol 11 (03) ◽  
pp. 245-252
Author(s):  
David R. Bickel
Keyword(s):  
Heart Rate ◽  
Time Scale ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Scaling Exponents ◽  
Absolute Deviation ◽  
Change Dynamics ◽  
Measures Of Dispersion ◽  
The Mean ◽  
Better Than

Three aspects of time series are uncertainty (dispersion at a given time scale), scaling (time-scale dependence) and intermittency (inclination to change dynamics). Simple measures of dispersion are the mean absolute deviation and the standard deviation; scaling exponents describe how dispersions change with the time scale. Intermittency has been defined as a difference between two scaling exponents. After taking a moving average, these measures give descriptive information, even for short heart rate records. For this data, dispersion and intermittency perform better than scaling exponents.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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